{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# NetworkX"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import datetime as dt\n",
        "from sklearn import covariance\n",
        "import networkx as nx\n",
        "from pylab import rcParams\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# yahoo finance is used to fetch data \n",
        "import yfinance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-04-06T00:37:00.812Z",
          "iopub.execute_input": "2022-04-06T00:37:00.818Z",
          "iopub.status.idle": "2022-04-06T00:37:02.344Z",
          "shell.execute_reply": "2022-04-06T00:37:02.337Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbols = ['AMD','AAPL','MSFT','GS', 'AMZN', 'GOOGL', 'TSLA', 'INTC']\n",
        "start = '2016-01-01'\n",
        "end = '2022-01-01'\n",
        "\n",
        "\n",
        "# Read data \n",
        "df = yf.download(symbols,start,end)['Adj Close']\n",
        "\n",
        "# View Columns\n",
        "df.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  8 of 8 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": "                 AAPL   AMD        AMZN       GOOGL          GS       INTC  \\\nDate                                                                         \n2016-01-04  24.220572  2.77  636.989990  759.440002  158.829605  28.746815   \n2016-01-05  23.613628  2.75  633.789978  761.530029  156.094925  28.611492   \n2016-01-06  23.151514  2.51  632.650024  759.330017  152.284210  27.977184   \n2016-01-07  22.174410  2.28  607.940002  741.000000  147.603821  26.928461   \n2016-01-08  22.291670  2.14  607.049988  730.909973  146.994095  26.649361   \n\n                 MSFT       TSLA  \nDate                              \n2016-01-04  49.488731  44.681999  \n2016-01-05  49.714500  44.686001  \n2016-01-06  48.811417  43.807999  \n2016-01-07  47.113625  43.130001  \n2016-01-08  47.258121  42.200001  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>AAPL</th>\n      <th>AMD</th>\n      <th>AMZN</th>\n      <th>GOOGL</th>\n      <th>GS</th>\n      <th>INTC</th>\n      <th>MSFT</th>\n      <th>TSLA</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2016-01-04</th>\n      <td>24.220572</td>\n      <td>2.77</td>\n      <td>636.989990</td>\n      <td>759.440002</td>\n      <td>158.829605</td>\n      <td>28.746815</td>\n      <td>49.488731</td>\n      <td>44.681999</td>\n    </tr>\n    <tr>\n      <th>2016-01-05</th>\n      <td>23.613628</td>\n      <td>2.75</td>\n      <td>633.789978</td>\n      <td>761.530029</td>\n      <td>156.094925</td>\n      <td>28.611492</td>\n      <td>49.714500</td>\n      <td>44.686001</td>\n    </tr>\n    <tr>\n      <th>2016-01-06</th>\n      <td>23.151514</td>\n      <td>2.51</td>\n      <td>632.650024</td>\n      <td>759.330017</td>\n      <td>152.284210</td>\n      <td>27.977184</td>\n      <td>48.811417</td>\n      <td>43.807999</td>\n    </tr>\n    <tr>\n      <th>2016-01-07</th>\n      <td>22.174410</td>\n      <td>2.28</td>\n      <td>607.940002</td>\n      <td>741.000000</td>\n      <td>147.603821</td>\n      <td>26.928461</td>\n      <td>47.113625</td>\n      <td>43.130001</td>\n    </tr>\n    <tr>\n      <th>2016-01-08</th>\n      <td>22.291670</td>\n      <td>2.14</td>\n      <td>607.049988</td>\n      <td>730.909973</td>\n      <td>146.994095</td>\n      <td>26.649361</td>\n      <td>47.258121</td>\n      <td>42.200001</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-04-06T00:37:02.350Z",
          "iopub.execute_input": "2022-04-06T00:37:02.354Z",
          "shell.execute_reply": "2022-04-06T00:37:03.668Z",
          "iopub.status.idle": "2022-04-06T00:37:03.600Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_of_years = 10\n",
        "start = dt.datetime.now() - dt.timedelta(int(365.25 * num_of_years))\n",
        "end = dt.datetime.now()"
      ],
      "outputs": [],
      "execution_count": 3,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2022-04-06T00:37:03.672Z",
          "iopub.status.busy": "2022-04-06T00:37:03.605Z",
          "iopub.execute_input": "2022-04-06T00:37:03.608Z",
          "iopub.status.idle": "2022-04-06T00:37:03.614Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = np.log1p(df.pct_change()).iloc[1:]\n"
      ],
      "outputs": [],
      "execution_count": 4,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2022-04-06T00:37:03.674Z",
          "iopub.status.busy": "2022-04-06T00:37:03.621Z",
          "iopub.execute_input": "2022-04-06T00:37:03.624Z",
          "iopub.status.idle": "2022-04-06T00:37:03.630Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Glasso algorithm\n",
        "edge_model = covariance.GraphicalLassoCV(cv=10)\n",
        "df /= df.std(axis=0)\n",
        "df = df.dropna()"
      ],
      "outputs": [],
      "execution_count": 5,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2022-04-06T00:37:03.676Z",
          "iopub.status.busy": "2022-04-06T00:37:03.636Z",
          "iopub.execute_input": "2022-04-06T00:37:03.639Z",
          "iopub.status.idle": "2022-04-06T00:37:03.644Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 6,
          "data": {
            "text/plain": "                AAPL       AMD      AMZN     GOOGL        GS      INTC  \\\nDate                                                                     \n2016-01-05 -1.369839 -0.190143 -0.272857  0.167467 -0.894614 -0.225666   \n2016-01-06 -1.066788 -2.396154 -0.097534 -0.176293 -1.273116 -1.072210   \n2016-01-07 -2.327546 -2.521819 -2.158518 -1.489003 -1.607987 -1.827207   \n2016-01-08  0.284681 -1.662793 -0.079374 -0.835441 -0.213221 -0.498277   \n2016-01-11  0.867000  2.344379  0.945759  0.179814  0.559371  0.827598   \n\n                MSFT      TSLA  \nDate                            \n2016-01-05  0.270017  0.002489  \n2016-01-06 -1.087529 -0.551459  \n2016-01-07 -2.100144 -0.433456  \n2016-01-08  0.181664 -0.605781  \n2016-01-11 -0.034018 -0.418002  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>AAPL</th>\n      <th>AMD</th>\n      <th>AMZN</th>\n      <th>GOOGL</th>\n      <th>GS</th>\n      <th>INTC</th>\n      <th>MSFT</th>\n      <th>TSLA</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2016-01-05</th>\n      <td>-1.369839</td>\n      <td>-0.190143</td>\n      <td>-0.272857</td>\n      <td>0.167467</td>\n      <td>-0.894614</td>\n      <td>-0.225666</td>\n      <td>0.270017</td>\n      <td>0.002489</td>\n    </tr>\n    <tr>\n      <th>2016-01-06</th>\n      <td>-1.066788</td>\n      <td>-2.396154</td>\n      <td>-0.097534</td>\n      <td>-0.176293</td>\n      <td>-1.273116</td>\n      <td>-1.072210</td>\n      <td>-1.087529</td>\n      <td>-0.551459</td>\n    </tr>\n    <tr>\n      <th>2016-01-07</th>\n      <td>-2.327546</td>\n      <td>-2.521819</td>\n      <td>-2.158518</td>\n      <td>-1.489003</td>\n      <td>-1.607987</td>\n      <td>-1.827207</td>\n      <td>-2.100144</td>\n      <td>-0.433456</td>\n    </tr>\n    <tr>\n      <th>2016-01-08</th>\n      <td>0.284681</td>\n      <td>-1.662793</td>\n      <td>-0.079374</td>\n      <td>-0.835441</td>\n      <td>-0.213221</td>\n      <td>-0.498277</td>\n      <td>0.181664</td>\n      <td>-0.605781</td>\n    </tr>\n    <tr>\n      <th>2016-01-11</th>\n      <td>0.867000</td>\n      <td>2.344379</td>\n      <td>0.945759</td>\n      <td>0.179814</td>\n      <td>0.559371</td>\n      <td>0.827598</td>\n      <td>-0.034018</td>\n      <td>-0.418002</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 6,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-04-06T00:37:03.648Z",
          "iopub.execute_input": "2022-04-06T00:37:03.653Z",
          "iopub.status.idle": "2022-04-06T00:37:03.685Z",
          "shell.execute_reply": "2022-04-06T00:37:03.678Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "edge_model.fit(df)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 7,
          "data": {
            "text/plain": "GraphicalLassoCV(cv=10)"
          },
          "metadata": {}
        }
      ],
      "execution_count": 7,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-04-06T00:37:03.690Z",
          "iopub.execute_input": "2022-04-06T00:37:03.692Z",
          "iopub.status.idle": "2022-04-06T00:37:04.138Z",
          "shell.execute_reply": "2022-04-06T00:37:04.143Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "p = edge_model.precision_\n",
        "rcParams['figure.figsize'] = 15,10\n",
        "sns.heatmap(p)\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1080x720 with 2 Axes>",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 8,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-04-06T00:37:04.148Z",
          "iopub.execute_input": "2022-04-06T00:37:04.155Z",
          "shell.execute_reply": "2022-04-06T00:37:04.290Z",
          "iopub.status.idle": "2022-04-06T00:37:04.296Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "p = pd.DataFrame(p)\n",
        "links = p.stack().reset_index()\n",
        "links.columns = ['var1', 'var2','value']\n",
        "links=links.loc[(abs(links['value']) > 0.17) &  (links['var1'] != links['var2'])]"
      ],
      "outputs": [],
      "execution_count": 9,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-04-06T00:37:04.302Z",
          "iopub.execute_input": "2022-04-06T00:37:04.305Z",
          "iopub.status.idle": "2022-04-06T00:37:04.311Z",
          "shell.execute_reply": "2022-04-06T00:37:04.318Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "G=nx.from_pandas_edgelist(links,'var1','var2', create_using=nx.Graph())\n",
        "pos = nx.spring_layout(G, k=0.2*1/np.sqrt(len(G.nodes())), iterations=20)\n",
        "plt.figure(3, figsize=(15, 15))\n",
        "nx.draw(G, pos=pos)\n",
        "nx.draw_networkx_labels(G, pos=pos)\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1080x1080 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "execution_count": 10,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-04-06T00:37:04.363Z",
          "iopub.execute_input": "2022-04-06T00:37:04.368Z",
          "iopub.status.idle": "2022-04-06T00:37:04.464Z",
          "shell.execute_reply": "2022-04-06T00:37:04.468Z"
        }
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.13",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kernelspec": {
      "argv": [
        "C:/Users/Tin Hang/Anaconda3\\python.exe",
        "-m",
        "ipykernel_launcher",
        "-f",
        "{connection_file}"
      ],
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "nteract": {
      "version": "0.28.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}